Using 3D Animation and Virtual Reality in Educations

This paper will look at the use of innovative and advanced technology teaching methods to support the goal of learning enhancement by using established teaching concepts. The primary aim is to explore the potential for using leading-edge virtual reality technology to help students learn and understand various concepts. The utility of the technology was examined by studying the latest 3D programming tools. Additionally, there were several styles of animation used. The results showed that Virtual Reality technologies can play a role in education and learning when supported by established learning and teaching concepts.

An Adaptive and Interactive Educational Game Platform for English Learning Enhancement using AI and Chatbot Techniques

As our world becomes more globalized, learning new languages will be an essential skill to communicate across countries and cultures and as a means to create better opportunities for oneself [4]. This holds especially true for the English language [5]. Since the rise of smartphones, there have been many apps created to teach new languages such as Babbel and Duolingo that have made learning new languages cheap and approachable by allowing users to practice briefly whenever they have a free moment for. This is where we believe those apps fail. These apps do not capture the interest or attention of the user’s for long enough for them to meaningfully learn. Our approach is to make a video game that immerses our player in a world where they get to practice English verbally with NPCs and engage with them in scenarios they may encounter in the real world [6]. Our approach will include using chatbot AI to engage our users in realistic natural conversation while using speech to text technology such that our user will practice speaking English [7].

Nobody is left behind. The beauty of Service Learning

School closures due to COVID-19 have brought significant disruptions to education. Service-learning interventions have offered significant opportunities to reduce and reverse the long-term negative effects and to empower the recovery process of pupils in difficulty. The study was carried out with 869 students enrolled in the Primary Education Sciences master’s degree course at the University of Palermo. The participants have been involved in the planning and implementation of targeted educational courses designed for the “fragile” pupils from 33 different schools in Palermo. The primary level pupils were provided with a total of 60,000 hours of recovery and learning enhancement activities in remote mode. Challenging teaching activities fostered cognitive and learning development of the pupils. The results reveal that the service-learning project led to the rediscovery of the beauty of inclusion, integration, and civic responsibility.

Quantifying the Utility of Low Resolution Brain Imaging:: Treatment of Infant Hydrocephalus

As low-field MRI technology is being disseminated into clinical settings, it is important to assess the image quality required to properly diagnose and treat a given disease. In this post-hoc analysis of an ongoing randomized clinical trial, we assessed the diagnostic utility of reduced-quality and deep learning enhanced images for hydrocephalus treatment planning. Images were degraded in terms of resolution, noise, and contrast between brain and CSF and enhanced using deep learning algorithms. Both degraded and enhanced images were presented to three experienced pediatric neurosurgeons accustomed to working in LMIC for assessment of clinical utility in treatment planning for hydrocephalus. Results indicate that image resolution and contrast-to-noise ratio between brain and CSF predict the likelihood of a useful image for hydrocephalus treatment planning. For images with 128x128 resolution, a contrast-to-noise ratio of 2.5 has a high probability of being useful (91%, 95% CI 73% to 96%; P=2e-16). Deep learning enhancement of a 128x128 image with very low contrast-to-noise (1.5) and low probability of being useful (23%, 95% CI 14% to 36%; P=2e-16) increases CNR improving the apparent likelihood of being useful, but carries substantial risk of structural errors leading to misleading clinical interpretation (CNR after enhancement = 5; risk of misleading results = 21%, 95% CI 3% to 32%; P=7e-11). Lower quality images not customarily considered acceptable by clinicians can be useful in planning hydrocephalus treatment. We find substantial risk of misleading structural errors when using deep learning enhancement of low quality images. These findings advocate for new standards in assessing acceptable image quality for clinical use.

AI-Based Assessment for Teaching and Learning Enhancement

This paper explores the use of Artificial Intelligence in the smart education learning process. Nowadays Artificial Intelligence grapes a place in the education field. This survey is giving justification that how the humans' mind is changed by AI. Here we go on the proposed model, experimentation, results, and Limitation of the system. These papers present various methods of Artificial Intelligence in student assessment for teacher and learning enhancement.